#!/usr/bin/python3
# coding=utf-8

# Copyright (c) 2025 Huawei Technologies Co., Ltd.
# This file is a part of the CANN Open Software.
# Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
# Please refer to the License for details. You may not use this file except in compliance with the License.
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
# See LICENSE in the root of the software repository for the full text of the License.
# ======================================================================================================================

import argparse
import os

import numpy as np


def gen_golden_data(work_path, mode, params=None):
    input_path = os.path.join(work_path, "input")
    if not os.path.exists(input_path):
        os.makedirs(input_path)
    output_path = os.path.join(work_path, "output")
    if not os.path.exists(output_path):
        os.makedirs(output_path)

    if params:
        m, n, k, base_m, base_n, is_bias = params.m, params.n, params.k, params.baseM, params.baseN, params.isBias
    else:
        m, n, k, base_m, base_n, is_bias = 2558, 2045, 128, 80, 64, True

    a_gm = np.random.uniform(-10, 10, [m, k]).reshape([m, k]).astype(np.float16)
    b_gm = np.random.uniform(-10, 10, [k, n]).reshape([k, n]).astype(np.float16)
    bias_gm = np.random.uniform(-10, 10, [n]).reshape([n]).astype(np.float32)
    c_gm = np.random.uniform(-10, 10, [m, n]).reshape([m, n]).astype(np.float32)

    if is_bias:
        golden_c = np.matmul(a_gm.astype(np.float32), b_gm.astype(np.float32), dtype=np.float32) + bias_gm
    else:
        golden_c = np.matmul(a_gm.astype(np.float32), b_gm.astype(np.float32), dtype=np.float32)

    for i in range(m):
        for j in range(n):
            upper_triangle_ignore_data = (mode == 'upper' and (int((i + base_m) / base_m) > int((j + base_n) / base_n)))
            lower_triangle_ignore_data = (mode == 'lower' and (int((i + base_m) / base_m) < int((j + base_n) / base_n)))
            if upper_triangle_ignore_data or lower_triangle_ignore_data:
                golden_c[i][j] = c_gm[i][j]

    a_gm.tofile(os.path.join(input_path, "a_gm.bin"))
    b_gm.tofile(os.path.join(input_path, "b_gm.bin"))
    bias_gm.tofile(os.path.join(input_path, "bias_gm.bin"))
    c_gm.tofile(os.path.join(input_path, "c_gm.bin"))
    golden_c.tofile(os.path.join(output_path, "golden.bin"))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('-m', type=str, default='upper', choices=['upper', 'lower'])
    args = parser.parse_args()
    gen_golden_data(os.getcwd(), args.m)
